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Missing Variables in Bayesian Regression. II,
RAND CORP SANTA MONICA CALIF
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The problem of parameter estimation in normal regression when some of the observations are missing was investigated. A Bayesian approach with vague prior distributions is taken. No assumption is made about the independent variables for which no observations are missing, but the missing components are assumed to be normally distributed with a mean that can depend on the other variables. Joint estimators of the parameters are obtained as the joint mode of the posterior distribution.
APPROVED FOR PUBLIC RELEASE